WHY FORECASTINGSales & Demand?

Decrease inventory write-offs. Demand forecasting for fast spoiling goods is essential to accurately plan their delivery and minimize waste due to shelf life expiration.

Optimal warehouse stock management. Demand prediction allows to efficiently manage warehouse stock and both cut the amount of illiquid items and meet customer demand. Machine learning methods in this case allow to take into account seasonal changes and general trend enhancing the forecasting quality.

Production and supply chain optimization. Foreasting demand level for particular goods allows to decrease production costs. This can be achieved by applying specific planning methods. Demand prediction also helps to cut logistics expenses by uniting consignments of goods or getting volume purchase discounts

To increase sales:

Decreasing unmet demand. Demand prediction allows to have sufficient amount of products and goods on shelves to fully meet clients' demand. This both helps to boost sales revenues and win customer loyalty, which ultimately leads to higher sales in future.

Efficient product range management. Knowing demand for particular products allows to remove illiquid products and introduce more liquid articles in order to enhance turnover the most liquid items

BUSINESSApplications

Demand forecasting in terms of predictive analytics can be applied to any business area and industry, as mathematical methods and engineering approaches are the same regardless of industry specialization. Depending on the industry, demand prediction can imply various specific tasks:

Selling goods to end customers

Bringing new good to sales channel

Sending goods and materials from warehouse stock to production

Sending the produce from production to warehousing

Providing "on demand" services (for example taxi services)

Any other related business tasks

How does the demandFORECASTING WORK?

Sales and demand are typically forecasted by methods of linear regression, gradient boosting with decision trees or recurrent neural networks based on historical data and some additional data on environment (weather conditions, market situation, currency exchange rates). While pre-processing data, a number of statistical metrics on historical demand levels during a few distorical periods are calculated. If demand is typically seasonal, the Data Science solution needs to take into account data for a few latest seasons. For example in case of forecasting intra-yearly seasonality, it is better to analyse data for 3 preceding years.

Business quite often does not have any historical data on product demand, for example, if the product is new or was not selling an the selected point of sale. In this case the predictive model uses generic statistics on similar product category. Products can be grouped differently depending on the industry and business area, for example by purpose, material, manufacturer, expiration date etc. Proper goods grouping is crucial for building a forecasting model of a high quality.

SOURCE DATA

Historical data on demand & sales volume

Reference data on product characteristics, their purpose and interchangeability

Historical market insights, such as:

production offering volume and competitors' pricing

statistical data

currency exchange rates

any other data, depending on the industry in question

Historical and forecasted data on the environment:

weather

transport availability

opening/closing points of customer attraction, which may influence customer flow and, as a result, demand

any other data, depending on the industry in question

Fayrix sales & demand forecasting solution will help you get accurate predictions to fully meet needs of your customers. Request quotation now to start forecasting demand for your product or service and improving your sales.